Feedbacks between the land surface and the atmosphere are expected to play a role in the response of the Earth system to climate change. Earth system models (ESMs) are designed to simulate these processes, but their implementation is only as good as scientific knowledge and technological capabilities allow. This puts ESMs in a dual role with respect to land-atmosphere feedbacks: On the one hand, they represent the most important tools researchers have for understanding land-atmosphere feedbacks and how they will affect the Earth system. On the other hand, they acquire substantial uncertainty as a result of limitations in their representation of feedback processes.
In this dissertation, I used ESMs within the context of both roles. I used them as tools for understanding how land-atmosphere feedbacks affect the Earth system and our understanding of it. I also used observational data to constrain land-atmosphere feedbacks in ESMs with the goal of evaluating and improving their ability to project future responses to climatic forcing.
The goal of my first science chapter was to evaluate how well ESMs represent seasonal-scale feedbacks between terrestrial water availability and atmospheric conditions. I developed a metric that was designed to use terrestrial water storage data from the Gravity Recovery and Climate Experiment (GRACE) satellites in conjunction with other remote sensing data sources for atmospheric temperature and humidity, precipitation, and downwelling shortwave radiation. First, I used GRACE to identify the months of the year when the land surface loses more water to evapotranspiration and runoff than it gains from precipitation. I then related the interannual water storage anomaly at the onset of this interval with subsequent atmospheric conditions as a measure of the land surface forcing on the atmosphere, as well as relating the atmospheric conditions with the subsequent water storage anomaly as a measure of the land surface response to atmospheric forcing. By calculating the equivalent quantities in an ensemble of ESMs, I demonstrated that the models tended to overestimate the strength of these relationships. These results are consistent with complementary efforts using other data sources and spatiotemporal scales, and suggest that overly simplistic representations of the heterogeneity within vegetation cover and bare soil could cause models to overestimate soil moisture feedbacks with the atmosphere.
My next chapter was partially motivated by a remaining question from the previous research: How much of the measured relationships was driven directly by interactions between soil moisture and the atmosphere, and how much is due to co-variability due to external forcing such as sea surface temperature (SST) anomalies. An additional motivation stemmed from the well-known relationship between the El Niño-Southern Oscillation (ENSO) and interannual variability in the growth rate of atmospheric CO2 concentrations. This relationship is attributed primarily to the response of tropical terrestrial ecosystems, and temperature has been widely implicated as the primary driver. However, there is an ongoing debate in the literature suggesting that hydrology is also an important driver, which, as I demonstrated in the previous chapter, is not independent of temperature.
To address these questions, I performed a set of experiments using the Energy Exascale Earth System Model (E3SM). I modified E3SM in order to decouple the interannual variability of soil moisture and SST, which allowed me to isolate the influence of each of these factors in the response of the Amazon rainforest to ENSO. I found that in E3SM, soil moisture served to amplify and extend the land surface response to ENSO in the Amazon. SST anomalies coupled with atmospheric circulation drove an immediate response, which coincides with the Amazon wet season. As the ecosystem was generally not water limited at this time, temperature played the dominant role in carbon cycle variability. However, soil moisture anomalies persisted into the dry season, intensifying and extending the response of both temperature and the terrestrial CO2 flux. This highlights the importance of considering the feedback between soil moisture and temperature when considering their relative importance as drivers of CO2 variability in the Amazon.
For my final science chapter, I used a global dataset of vertically resolved soil radiocarbon observations to evaluate the representation of soil carbon processes in the Community Land Model (CLM) and the E3SM land model (ELM). I found that while ELM slightly overestimated radiocarbon ages in temperate latitudes, it underestimated them in boreal and tropical latitudes, particularly at depth, enough to lead to a young bias globally. CLM, on the other hand, underestimated radiocarbon ages at all depths, latitudes, and vegetation types. This suggests that carbon was cycling through the soil too quickly in the models, leading them to overestimate the rate at which soils could sequester carbon in response to increasing atmospheric CO2 concentrations.
I used the observed radiocarbon profiles to constrain kinetic rate constants and transfer coefficients in ELM, which improved the young age bias and improved a low bias in the tropical soil carbon stock. Constraining the model increased the global soil carbon stock as a result of the improvements in the tropics, and reduced the contribution of soil to the terrestrial carbon sink. This suggests that soil is likely to respond very slowly to increasing atmospheric CO2 concentrations, and is unlikely to serve as a short-term carbon sink.
These three chapters illustrate the range applications for ESMs in the context of land-atmosphere feedbacks. ESMs can be evaluated for how well they reproduce observations of relationships between multiple climatic variables, as in the first chapter. ESMs can be used to understand land-atmosphere feedbacks in the Earth system, by isolating factors that are difficult or impossible to disentangle in nature, as in the second chapter. ESMs can be constrained by observations in order to reduce uncertainty in their simulation of land-atmosphere feedbacks, as in the final chapter. This dissertation represents steps toward understanding how energy, water, and carbon flow through terrestrial ecosystems, and improving the representation of these processes in ESMs.